The classification of a signal, the detection of an event or change in a system through the analysis of a signal, or the prediction of a change or event are all classical problems in many areas of engineering.
The classification of a signal into a library composed of several distinct groups is a classical problem in the signal processing literature. The objective can be stated simply: Given a test signal, it is desired to know if the signal is likely to be a member of previously characterized groups that comprise the library. Also, it is desired to know the accuracy (confidence) of the assignment of the signal. This problem presents itself in most areas of engineering practice. This problem is also encountered in the analysis of biological data, particularly behavioral data, and in clinical applications. For example, in the course of investigations of animal behavior, one often wants to characterize the degree of similarity in the behavior of a specific animal against previously observed control data and against data obtained after the administration of drugs.
Clinically, the classification problem is encountered during diagnostic procedures. Given a patient's ECG or EEG, it is desirable to know the probability that the signal correlates with healthy, age-matched control subjects and/or the probability that the signal correlates with a set of well characterized clinical signals of a particular abnormality or condition.
In general, it would be desirable to provide a system and method for categorizing measurable time dependent data with a relatively high probability correlation with one or more sets from among a group of sets that define a library of well characterized conditions or groups.